Brother's Keeper or Only Child? Black Middle Class Responses to Residential Mobility Initiatives in Prince George's County, MD
Bibliographic record
Abstract
The implications of urban revitalization, gentrification, and residential migration have attracted widespread interest and ongoing debate among scholars across a range of disciplines. While a significant body of literature explores race and class interactions within urban gentrifying neighborhoods, few have examined the environments that await those displaced by this process. This study explores the social and political impact of urban gentrification and class stratification within the black community by examining responses of black middle class residents in Prince George’s County, MD to the growing in-migration of low-income and minority residents from Washington, DC. Drawing on data from the U.S. Census Bureau, a multi-neighborhood sample of ninety-five black middle class residents of Prince George’s County, and informal interviews with subject-area experts, this study explores how race and class shape residential decisions and their impact on residential mobility initiatives. Residents responded to a 26-item survey that covered demographic information, political and community engagement, and their attitudes and beliefs about the poor, changes in their community, and racial unity and responsibility. Findings from cross tabulations and binary logistic regression indicate that lower middle class residents are the most likely to resist in-migration by exiting their communities and/or voting against proposals to create affordable housing options. Core and upper middle class residents were the most likely to stay in their neighborhoods despite increases in low-income migration, to vote in support of policies to create affordable housing options and to believe their responsibility to poor blacks could include sharing residential space.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".